_base_ = "./htc_r50_fpn_1x_coco.py"
model = dict(
    pretrained="open-mmlab://resnext101_64x4d",
    backbone=dict(
        type="ResNeXt",
        depth=101,
        groups=64,
        base_width=4,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
        dcn=dict(type="DCN", deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True),
    ),
)
# dataset settings
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True, with_seg=True),
    dict(
        type="Resize",
        img_scale=[(1600, 400), (1600, 1400)],
        multiscale_mode="range",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="SegRescale", scale_factor=1 / 8),
    dict(type="DefaultFormatBundle"),
    dict(
        type="Collect",
        keys=["img", "gt_bboxes", "gt_labels", "gt_masks", "gt_semantic_seg"],
    ),
]
data = dict(samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type="EpochBasedRunner", max_epochs=20)
